Transcript AI-05
Fuzzy Expert System An expert might say, “ Though the power transformer is slightly overloaded, I can keep this load for a while”. Another expert in the same domain can understand it. But, a knowledge engineer would have difficulties, providing a computer with the same level of understanding. How can we represent expert knowledge that use vague and ambiguous terms in computer? An expert system that uses fuzzy logic instead of Boolean logic is known as Fuzzy expert system. A fuzzy expert systems is collection of fuzzy rules and membership functions that are used to reason about data. Fuzzy logic is a logic that describes fuzziness. As fuzzy logic attempts to model human’s sense of words, decision making and common sense, it is leading to more human intelligent machines. Fuzzy logic was introduced in the 1930 by Jan Lukasiewicz, a Polish Philosopher(extended the truth values between 0 to 1) Later, 1937 Max Black define first sample fuzzy set. In 1965, Lotif Zadeh rediscovered fuzziness, identified and explored it. Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership rather than the crisp membership of classical binary logic. Unlike two-valued Boolean logic, fuzzy logic is multi valued. 0 0 01 Boolean Logic 1 1 0 0 0.2 0.4 0.6 Multivalued Logic 0.8 1 1 Classical set theory is governed by a logic that uses one of only two values: true and false. The basic idea of fuzzy set theory is that an element belongs to a fuzzy set with a certain degree of membership. Thus a proposition is not either true or false. Classical set theory imposes a sharp boundary on this set and gives each member of the set the value of 1, and all members that are not within the set a value of 0. This is known as the principle of dichotomy. Consider following classical paradox: The barber of a village gives a hair cut only to those who do not cut their hair themselves. Question: Who cut the barber hair? Boolean logic: This assertion contains a contradiction. Fuzzy logic: The barber cuts and doesn’t cut his own hair Degree of Membership of “tall men” Name Height (cm) Degree of membership Crisp Fuzzy Rahim 208 1 1.00 Karim 205 1 1.00 Ram 198 1 .98 Sam 181 1 .82 Jodu 179 0 .78 Modu 172 0 0.24 Abdul 167 0 0.15 Anis 158 0 0.06 Montu 155 0 0.01 Robin 152 0 0.00 Red line for ‘Crisp’ sets and Blue line for ‘Fuzzy’ sets of tall men A fuzzy set is defined as a set with fuzzy boundaries. Let X be the universe of discourse and its elements be denoted as x. In classical set theory, crisp set A of X is defined as function fA(x) called the characteristic function of A. f A ( x) : X 0,1, f A ( x) 1 If x Є A 0 If x Є A In the fuzzy theory, fuzzy set A of universe X is defined by the function A(x) called the membership function of set A. A ( x) : X [0,1], A ( x) 1 A ( x) 0 0 A ( x)1 If x is totally in A; If x is not in A; If x is partly in A; A conditional statement in the form: If x is A; then y is B, Where x and y are linguistic variables and A & B are linguistic values determined by fuzzy sets. Examples: Rule1: If Speed is fast Then stopping_distance is long Rule 2: If Speed is slow Then stopping_distance is short Fuzzy inference is a process of mapping from a given input to an output by using the theory of Fuzzy sets. The process of reasoning based on fuzzy logic. Fuzzy inference includes four steps: Fuzzification of the input variables Rule evaluation Aggregation of the rule outputs Defuzzification Rule1: If x is A3 OR y is B1 Then z is C1 Rule1: If project_funding is adequate OR project_staffing is small Then risk is low Rule2: If x is A2 AND y is B2 Then z is C2 Rule2: If project_funding is marginal AND project_staffing is large Then risk is normal Rule3: If x is A1 Then z is C3 Rule3: If project_funding is inadequate Then risk is high The first step of fuzzy inference; the process of mapping crisp (numerical) inputs into degrees to which these inputs belong to respective fuzzy sets. Example: Membership function of project_stuffing is small (B1) and large (B2) to the degree of 0.1 and 0.7. The second step is to take the fuzzified inputs, (x=A1)=0.5, (x=A2)=0.2, (y=B1)=0.1 and (y=B2)=0.7, and apply them to the antecedents of the Fuzzy rules. Example: Rule1: If x is A3 (0.0) OR y is B1 (0.1) Then z is C1 (0.1) c1(z)=max[A3(x), B1(y)]=max[0.0, 0.1]=0.1 Rule2: If x is A2 (0.2) AND y is B2 (0.7) Then z is C1 (0.2) c2(z)=min[A2(x), B2(y)]=min[0.2, 0.7]=0.2 The result of the antecedent evaluation can be applied to the membership function of the consequent. Aggregation is the process of unification of the outputs of all rules. The last step in the fuzzy inference process is defuzzification. The input for the defuzzification process is the aggregate output fuzzy set and the output is a single number. Example: Risk is 67.4% [Negnevitsky, 2001] M. Negnevitsky “ Artificial Intelligence: A guide to Intelligent Systems”, Pearson Education Limited, England, 2002. [Russel, 2003] S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Prentice Hall, 2003, Second Edition [Patterson, 1990] D. W. Patterson, “Introduction to Artificial Intelligence and Expert Systems”, Prentice-Hall Inc., Englewood Cliffs, N.J, USA, 1990. [Lindsay, 1997] P. H. Lindsay and D. A. Norman, Human Information Processing: An Introduction to Psychology, Academic Press, 1977.